Innovative Diagnostics for Helminth Parasite Infections

Project details:

Helminth (worm) parasites cause ill health and poor productivity in farm animals worldwide. Control relies largely on anthelmintic drugs, either applied en masse to entire herds based on perceived infection risk, or guided by treatment decisions made using sub-optimal diagnostic tests. Improved diagnostic and prognostic tools could catalyse a step-change in helminth control, enabling evidence-based treatment decisions, reduced resistance risk, and more accurate parasite risk forecasting. This project will employ state-of-the-art molecular and computational biology methods to develop sensitive, specific molecular diagnostic and prognostic tools for locally relevant veterinary helminth infections. The first stage of the project will identify and measure extracellular biomarkers in blood samples from helminth-infected animals. These data will then be analysed computationally, using network analyses and a kind of artificial intelligence called supervised machine learning, to identify patterns of molecules associated with parasite infection. The aim is that these “molecular fingerprints” will provide biomarkers that form the basis of improved diagnostic and prognostic tests, that will support improved parasite control in production animals.

This project will be supervised by Dr Paul McVeigh and Dr Nikki Marks of Queen’s University School of Biological Sciences.

All applications MUST be submitted through https://dap.qub.ac.uk/portal/user/u_login.php.

Specific skills/experience required by applicants:

Applications welcome from candidates with demonstrable molecular biology and/or computational/coding experience.

All applicants must meet the academic entry requirements: https://www.qub.ac.uk/courses/postgraduate-research/biological-sciences-phd.html#entry